With advances in natural language processing (NLP), there is an increasing demand to improve automated dialog systems to perform user tasks. A shortcoming of conventional automated dialog systems is the assumption that the user has knowledge of the dialog system's capacities before interacting with the dialog system. Conventional dialog systems assume that their users are aware of the potential different methods of inputting speech information. However, the users might not know about the functionalities of the automated dialog system or how to properly trigger these functionalities.
Conventional dialog systems use hardcoded help prompts to provide the user with help information. However, such help information does not incorporate the dialog and therefore does not follow the flow of the conversation. Accordingly, such help information does not adapt to the previously collected information in the dialog. Therefore, the hardcoded help prompts in conventional dialog systems are typically much too context agnostic. Such hardcoded help information may guide users to phrase their input in a format that is not well supported by the natural language understanding (NLU) of the dialog system, resulting in problems handling the received user speech input by the dialog system.
Because conventional dialog systems use hardcoded help prompts, such systems also fail to update their help prompts with updated data over time. As a dialog application evolves, with the addition of new data or new user information, the dialog system may require different information from the user. The user is often unaware of the evolving capabilities of the dialog system. Hardcoded help prompts do not allow the user to receive help prompts that provide the most updated help data mirroring the current state of the dialog system. Accordingly, current dialog systems fail to provide context aware help prompts that track the dialog and provide updated help information to the user reflecting the current state of the dialog system.